The incidence of type 2 diabetes mellitus has been rising worldwide, and its associated costs are growing rapidly. It is therefore imperative that all major factors that contribute to the development of diabetes are identified. While the causal role of adiposity in the development of insulin resistance (the pathophysiology underlying type 2 diabetes) has long been recognized, there is increasing observational evidence that low muscle mass also contributes independently to poor glucose metabolism. This secondary data analysis R21 application proposes to quantify the independent influences of fat and muscle mass in the development of insulin resistance and dysglycemia, using prospective cohort data over 15 waves (and 20 years) from a large, well-characterized, cohort of women transitioning through the menopause, a transition often accompanied by substantial increase in glucose intolerance and fat mass, and decrease in muscle mass. To date, nearly all large studies of muscle mass and dysglycemia have been cross-sectional and have not established a causal role for low muscle mass in the development of diabetes. The Study of Women's Health Across the Nation (SWAN), which annually collected a rich set of biological and medical data, as well as body composition from dual-energy X-ray absorptiometry (DXA) scans and bioelectrical impedance (BEI) measurements, represents a uniquely valuable resource with which to tease apart the independent contributions of fat mass and muscle mass to the development of type 2 diabetes. This project will leverage the large changes in glucose metabolism and body composition over the 15 waves (baseline plus 14 follow ups) in this cohort and the variability in their magnitude and timing across the sample to identify the causal effects of fat and muscle mass on insulin resistance and dysglycemia, and separate out the reverse causal effects of diabetes on muscle mass. The project specific aims are: 1) To quantify the independent contributions of decreasing muscle mass and increasing fat mass to longitudinal changes in glucose metabolism; 2) To compare the predictive ability of whole body muscle mass measured by DXA against that estimated by BEI to predict longitudinal changes in glucose metabolism; 3) To determine if overt diabetes is associated with greater decreases over time in muscle mass, and if this association is mediated by inflammation. This research will advance our understanding of a) the independent roles of fat and muscle mass in the development of diabetes risk; and b) the clinical applicability of the simpler, less expensive, easier to use, BEI based measurement of muscle mass. This work will inform the development and testing of the optimal exercise prescription that is both time-efficient and most effective in preventing diabetes and ultimately influence clinical guidelines and public health recommendations aimed at combating the global diabetes epidemic.